Setup

Load packages

library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
library(scales) # scales on plots
library(patchwork) # for combining plots
library(lme4) # for linear mixed models
library(sjPlot) # for lmm output

R / package versions used

R.Version() 
## $platform
## [1] "x86_64-w64-mingw32"
## 
## $arch
## [1] "x86_64"
## 
## $os
## [1] "mingw32"
## 
## $system
## [1] "x86_64, mingw32"
## 
## $status
## [1] ""
## 
## $major
## [1] "4"
## 
## $minor
## [1] "0.5"
## 
## $year
## [1] "2021"
## 
## $month
## [1] "03"
## 
## $day
## [1] "31"
## 
## $`svn rev`
## [1] "80133"
## 
## $language
## [1] "R"
## 
## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
## 
## $nickname
## [1] "Shake and Throw"
packageVersion('here')
## [1] '1.0.1'
packageVersion('tidyverse')
## [1] '1.3.1'
packageVersion('scales') 
## [1] '1.2.0'
packageVersion('patchwork')
## [1] '1.1.1'
packageVersion('lme4') 
## [1] '1.1.27.1'
packageVersion('sjPlot') 
## [1] '2.8.10'

Read pre-wrangled data

(see 00-wrangling-setup.Rmd script)

# here::here()
apt <- readRDS(here("data", "apt-data.rds"))
apt <- as_tibble(apt)
head(apt)
## # A tibble: 6 x 46
##   session    group ppt   selection selected continuation continue L1_bsl
##   <fct>      <fct> <fct> <fct>        <dbl> <fct>           <dbl> <fct> 
## 1 pre-degree first S70   selected         1 withdrew            1 0     
## 2 pre-degree first S41   selected         1 withdrew            0 0     
## 3 pre-degree first S63   selected         1 withdrew            0 0     
## 4 pre-degree first S90   selected         1 withdrew            0 0     
## 5 pre-degree first S60   selected         1 withdrew            0 0     
## 6 pre-degree first S96   selected         1 continuing          1 1     
## # ... with 38 more variables: self_rating <dbl>, bsl_years <dbl>,
## #   nback_lett <dbl>, nback_spat <dbl>, nback_comb <dbl>, corsi_bspan <dbl>,
## #   corsi_score <dbl>, corsi_corr <dbl>, corsi_mspan <dbl>, kirk_ceil <dbl>,
## #   kirk_raw <dbl>, kirk_acc <dbl>, kbit_ceil <dbl>, kbit_raw <dbl>,
## #   kbit_acc <dbl>, dspan_mem <dbl>, dspan_corr <dbl>, dspan_time <dbl>,
## #   mr2d_acc <dbl>, mr2d_rt <dbl>, mr2d_sats <dbl>, mr3d_acc <dbl>,
## #   mr3d_rt <dbl>, mr3d_sats <dbl>, bis_tot <dbl>, bis_att <dbl>, ...

Filter out participants who did not progress beyond interview

apt <- apt %>% filter(selection == "selected")

Filter out participants with deaf family members / L1 BSL

apt <- apt %>% filter(L1_bsl != 1)

Convert data from long format to wide format

apt_wide <- apt %>% 
  tidyr::pivot_wider(names_from = session,
                     values_from = c(nback_lett:grade_terp))

Plots & correlations

# set up a theme for the plots
theme_details <- theme_bw() + 
    theme(panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank(),
          panel.border = element_rect(colour = "black", size = 0.5),
          plot.title = element_text(size = 15, face = "bold"),
          plot.title.position = "plot",
          axis.title.x = element_text(size = 14, face = "bold"),
          axis.title.y = element_text(size = 14, face = "bold"),
          axis.text.x = element_text(size = 14, colour = "black"),
          axis.text.y = element_text(size = 14, colour = "black"),
          legend.position = "none")

BSL: predicted impact

First we look at the predictor variables which we hypothesised may have a relationship with BSL outcomes.

Corsi Blocks

vs BSL Grades
# Initial visuospatial skill vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Initial visuospatial skill vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial Corsi Blocks score")

a <- apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") 

# correlation
a %>% summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000657
# Initial visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial visuospatial skill vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial Corsi Blocks score")

#correlation
apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000256
# 1st year visuospatial skill vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "1st year visuospatial skill vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "1st year Corsi Blocks score ")

# correlation
apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.153
# 1st year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "1st year visuospatial skill vs. 2nd year BSL grades", 
       y = "1st year BSL grade", 
       x = "2nd year Corsi Blocks score ")

# correlation
apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0867
# 2nd year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(7, 10)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year visuospatial skill vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "2nd year Corsi Blocks score ")

# correlation
apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00871
vs BSL-SRT

Does Corsi Blocks performance relate to BSL Sentence Reproduction Task?

# Initial Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  coord_cartesian(xlim = c(6, 10)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial Corsi Blocks score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial Corsi Blocks score")

# correlation
apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.101
# 2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores
( fig4a <- apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "a) 2nd year Visuospatial WM vs. 3rd year BSL-SRT score", 
       y = "3rd year BSL-SRT score", 
       x = "2nd year Corsi Blocks score") )

# correlation
apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.609
# linear model
lm(`bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating,
                  data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
##  0.50226 -1.33521 -1.85779 -3.44244 -0.01693  3.98307  2.39842 -0.23138 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -6.3634     7.0008  -0.909   0.4051  
## `corsi_corr_2nd year`   2.1591     0.7907   2.731   0.0412 *
## self_rating             0.1072     0.9523   0.113   0.9147  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.793 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.6099, Adjusted R-squared:  0.4539 
## F-statistic: 3.909 on 2 and 5 DF,  p-value: 0.09503
# 3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores
( fig4b <- apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "b) 3rd year Visuospatial WM vs. 3rd year BSL-SRT score", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year Corsi Blocks score") )

# correlation
apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.301
# linear model
lm(`bsl_srt_3rd year` ~ `corsi_corr_3rd year` +
                    self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2436 -2.3397  0.2179  1.3910  5.9103 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -1.9487     9.3428  -0.209    0.842
## `corsi_corr_3rd year`   1.6346     1.0570   1.547    0.173
## self_rating            -0.1538     1.1578  -0.133    0.899
## 
## Residual standard error: 3.409 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.3029, Adjusted R-squared:  0.07051 
## F-statistic: 1.303 on 2 and 6 DF,  p-value: 0.3388

Combine plots for Figure 4 using patchwork:

( fig4 <- (fig4a | fig4b) )

ggsave("plots/fig4.jpeg", plot=fig4, width = 12, height = 5, units = "in")

2D Mental Rotation

vs BSL Grades

Does 2D Mental Rotation performance relate to BSL grades?

# Initial 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "Initial 2D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial 2D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0939
# Initial 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial 2D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00345
# 1st year 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "1st year 2D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "1st year 2D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.482
# linear model
lm(`grade_bsl_1st year` ~ `mr2d_sats_1st year` + self_rating,
                  data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mr2d_sats_1st year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1327 -1.3366  0.5675  1.4788  5.5454 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           69.9338     6.1350  11.399 2.73e-05 ***
## `mr2d_sats_1st year`  -5.3368     3.0141  -1.771    0.127    
## self_rating           -0.3072     1.8532  -0.166    0.874    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.991 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.4848, Adjusted R-squared:  0.3131 
## F-statistic: 2.824 on 2 and 6 DF,  p-value: 0.1367
# interestingly, a negative correlation, but not significant

# 1st year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "1st year 2D Mental Rotation score vs. 2nd year BSL grades", 
       y = "1st year BSL grade", 
       x = "2nd year 2D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.121
# 2nd year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  coord_cartesian(xlim = c(-1.3, 1.3)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year 2D Mental Rotation vs. 2nd year BSL Grades", 
       y = "2nd year BSL Grades", 
       x = "2nd year 2D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.265
# linear model
lm(`grade_bsl_2nd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.458  -7.042   2.668   5.846   7.147 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            59.653      5.782  10.316 1.74e-05 ***
## `mr2d_sats_2nd year`    4.438      2.702   1.642    0.144    
## self_rating             1.660      2.466   0.673    0.522    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.14 on 7 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.3094, Adjusted R-squared:  0.1121 
## F-statistic: 1.568 on 2 and 7 DF,  p-value: 0.2737
vs BSL-SRT

Does 2D Mental Rotation performance relate to BSL Sentence Reproduction Task?

# 2nd year 2D Mental Rotation score vs. 2nd year BSL-SRT scores
# just 4 data points here

# 3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year 2D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.191
# linear model
lm(`bsl_srt_3rd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3836 -0.7359  1.0129  1.5794  3.8603 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             9.865      2.841   3.473   0.0133 *
## `mr2d_sats_3rd year`    3.108      2.074   1.499   0.1846  
## self_rating             1.057      1.149   0.920   0.3932  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.438 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.2906, Adjusted R-squared:  0.05418 
## F-statistic: 1.229 on 2 and 6 DF,  p-value: 0.357

3D Mental Rotation

vs BSL Grades

Does 3D Mental Rotation performance relate to BSL grades?

# Initial 3D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "Initial 3D Mental Rotation score vs. 1st year BSL grades", 
       y = "1st year BSL grade", 
       x = "Initial 3D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0671
# linear model
lm(`grade_bsl_1st year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mr3d_sats_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.040  -5.622  -1.414   7.158  18.157 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             65.4722     5.8011  11.286 2.56e-09 ***
## `mr3d_sats_pre-degree`   1.5614     1.3900   1.123    0.277    
## self_rating             -0.4048     2.1124  -0.192    0.850    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.884 on 17 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.0691, Adjusted R-squared:  -0.04042 
## F-statistic: 0.6309 on 2 and 17 DF,  p-value: 0.5441
# Initial 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial 3D Mental Rotation score vs. 2nd year BSL grades", 
       y = "2nd year BSL grade", 
       x = "Initial 3D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000282
# 2nd year 3D Mental Rotation score vs. 2nd year BSL grades
( fig5b <- apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-1.2, 2.6)) +
  theme_minimal() + theme_details + 
  labs(title = "b) 2nd year 3D Mental Rotation vs. 2nd year BSL Grade", 
       y = "2nd year BSL Grade", 
       x = "2nd year 3D Mental Rotation score") )

# correlation
apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.386
# linear model
lm(`grade_bsl_2nd year` ~ `mr3d_sats_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr3d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.5850 -5.8497  0.7585  3.7502  9.7940 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           61.8296     4.9508  12.489 5.48e-07 ***
## `mr3d_sats_2nd year`   4.6447     2.0988   2.213   0.0542 .  
## self_rating           -0.7094     2.3738  -0.299   0.7719    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.986 on 9 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.3924, Adjusted R-squared:  0.2574 
## F-statistic: 2.906 on 2 and 9 DF,  p-value: 0.1062
vs BSL-SRT

Does 3D Mental Rotation performance relate to BSL Sentence Reproduction Task?

# Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores
( fig5a <- apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-2.7, 1.2)) +
  theme_minimal() + theme_details + 
  labs(title = "a) Initial 3D Mental Rotation vs. 3rd year BSL-SRT", 
       y = "3rd year BSL-SRT score", 
       x = "Initial 3D Mental Rotation score") )

# correlation
apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.369
lm(`bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.8469 -0.7479 -0.2005  1.9028  3.2185 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            14.55018    2.71305   5.363  0.00172 **
## `mr3d_sats_pre-degree`  1.90099    1.05072   1.809  0.12040   
## self_rating            -0.02618    1.06583  -0.025  0.98120   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.243 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.3692, Adjusted R-squared:  0.1589 
## F-statistic: 1.756 on 2 and 6 DF,  p-value: 0.2511
# 2nd year 3D Mental Rotation score vs. 2nd year BSL-SRT scores
# only 4 data points here

# 3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year 3D Mental Rotation score")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.123

Combine plots for Figure 5 using patchwork:

( fig5 <- (fig5a | fig5b) )

ggsave("plots/fig5.jpeg", plot=fig5, width = 12, height = 5, units = "in")

MLAT Number Learning

vs BSL Grades
# Initial MLAT Number Learning Accuracy vs. BSL grades 1st year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`grade_bsl_1st year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 1st year", 
       x = "MLAT Number Learning Accuracy", y = "BSL grade 1st year")

# correlation
apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0215
# linear model
lm(`grade_bsl_1st year` ~ `mlat_acc_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mlat_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.9838  -7.0550  -0.3413   7.5274  14.1245 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            54.7371    13.3127   4.112 0.000728 ***
## `mlat_acc_pre-degree`   8.4969    12.4720   0.681 0.504872    
## self_rating             0.7031     2.3654   0.297 0.769878    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.11 on 17 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.02658,    Adjusted R-squared:  -0.08794 
## F-statistic: 0.2321 on 2 and 17 DF,  p-value: 0.7954
# Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`grade_bsl_2nd year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year", 
       x = "MLAT Number Learning Accuracy", y = "BSL grade 2nd year")

# correlation
apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0333
# linear model
lm(`grade_bsl_2nd year` ~ `mlat_acc_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mlat_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.216  -7.027   3.224   7.449  17.760 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             49.048     16.575   2.959   0.0104 *
## `mlat_acc_pre-degree`   13.098     15.646   0.837   0.4166  
## self_rating              1.405      2.991   0.470   0.6459  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.33 on 14 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.04827,    Adjusted R-squared:  -0.08769 
## F-statistic: 0.355 on 2 and 14 DF,  p-value: 0.7073
vs BSL-SRT
# Initial MLAT Number Learning Accuracy vs. BSL-SRT 3rd year
apt_wide %>%
  filter(`mlat_acc_pre-degree` != "na") %>% 
  filter(`bsl_srt_3rd year` != "na") %>% 
  ggplot(aes(x = `mlat_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title= "Initial MLAT Number Learning Accuracy vs. BSL-SRT score 3rd year", 
       x = "MLAT Number Learning Accuracy")

# correlation
apt_wide %>% 
  filter(`mlat_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`mlat_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000354

BSL: no predicted impact

Now we look at the predictor variables which we hypothesised were unlikely to have a relationship with BSL outcomes.

Kirklees Sentence Reading

vs BSL Grades
# Kirklees Sentence Reading pre-degree vs. BSL grades 1st year
( fig6a <- apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  coord_cartesian(xlim = c(.49, .96)) +
  theme_minimal() + theme_details + 
  labs(title = "a) Initial English vocab vs. 1st year BSL Grade", 
       y = "1st year BSL Grade", x = "Initial Kirklees accuracy") )

# correlation
apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.188
# linear model
kirk0_bsl1 <- lm(`grade_bsl_1st year` ~ `kirk_acc_pre-degree` +
                   self_rating# + age_s1 # removing age for public version of code
                 , data = apt_wide)
summary(kirk0_bsl1)
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `kirk_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.5382  -4.7901   0.6237   3.9859  13.0213 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             32.187     11.757   2.738   0.0110 *
## `kirk_acc_pre-degree`   35.345     13.333   2.651   0.0135 *
## self_rating              1.318      1.356   0.972   0.3402  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.713 on 26 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2161, Adjusted R-squared:  0.1558 
## F-statistic: 3.583 on 2 and 26 DF,  p-value: 0.04223
tab_model(kirk0_bsl1, show.aic = T)
  grade_bsl_1st year
Predictors Estimates CI p
(Intercept) 32.19 8.02 – 56.35 0.011
kirk_acc_pre-degree 35.35 7.94 – 62.75 0.013
self_rating 1.32 -1.47 – 4.11 0.340
Observations 29
R2 / R2 adjusted 0.216 / 0.156
AIC 205.624
# Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.55, .95)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading pre-degree")

# correlation
apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0189
# Kirklees Sentence Reading 1st year vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "Kirklees Sentence Reading after 1 year")

# correlation
apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0417
# Kirklees Sentence Reading 1st year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading after 1 year")

# correlation
apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0853
# Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.6, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Kirklees Sentence Reading 2nd year")

# correlation
apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00824
vs BSL-SRT

Does Kirklees Sentence Reading performance relate to BSL Sentence Reproduction Task?

# Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
( fig6b <- apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2, position="jitter", seed = 42) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  coord_cartesian(xlim = c(.56, .93)) +
  theme_minimal() + theme_details + 
  labs(title = "b) Initial English vocab vs. 3rd year BSL-SRT score", 
       y = "3rd year BSL-SRT score", x = "Initial Kirklees accuracy") )

# correlation
apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.237
lm(`bsl_srt_3rd year` ~ `kirk_acc_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `kirk_acc_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1089 -1.8031 -0.1845  2.5358  4.6470 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -2.1142     9.3522  -0.226    0.829
## `kirk_acc_pre-degree`  16.9416    10.8402   1.563    0.169
## self_rating             0.8558     1.0985   0.779    0.466
## 
## Residual standard error: 3.398 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.3071, Adjusted R-squared:  0.0761 
## F-statistic: 1.329 on 2 and 6 DF,  p-value: 0.3327
# 2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year Kirklees Sentence Reading score")

# correlation
apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.149

Combine plots for Figure 6 using patchwork:

( fig6 <- (fig6a | fig6b) )

ggsave("plots/fig6.jpeg", plot=fig6, width = 12, height = 5, units = "in")

Summarising Task

vs BSL Grades
# Summarising pre-degree vs. BSL Grades 1st year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. BSL Grades 1st year", 
       y = "1st year BSL grade", x = "Summarising pre-degree")

# correlation
apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00209
# Summarising pre-degree vs. BSL Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. BSL Grades 2nd year", 
       y = "2nd year BSL grade", x = "Summarising pre-degree")

# correlation
apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0588
vs BSL-SRT
# Summarising pre-degree vs. BSL-SRT 3rd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. BSL-SRT 3rd year", 
       y = "BSL-SRT 3rd year", x = "Summarising pre-degree")

# correlation
apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0108

KBIT-2 Matrices

vs BSL Grades
# KBIT-2 Matrices pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "KBIT-2 Matrices pre-degree")

# correlation
apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0115
# KBIT-2 Matrices pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices pre-degree")

# correlation
apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0134
# KBIT-2 Matrices 1st year vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "KBIT-2 Matrices 1st year")

# correlation
apt_wide %>% 
  filter(`kbit_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0273
# KBIT-2 Matrices 1st year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices 1st year")

# correlation
apt_wide %>% 
  filter(`kbit_acc_1st year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000593
# KBIT-2 Matrices 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "KBIT-2 Matrices 2nd year")

# correlation
apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.167
vs BSL-SRT

Does KBIT-2 Matrices performance relate to BSL Sentence Reproduction Task?

# Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.84, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial KBIT-2 Matrices score")

# correlation
apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00494
# 2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year KBIT-2 Matrices score")

# correlation
apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0933

Dual N-Back

vs BSL Grades
# Dual N-Back pre-degree vs. BSL Grades 1st year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. BSL Grades 1st year", 
       y = "1st year BSL grade", x = "Dual N-Back pre-degree")

# correlation
apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0190
# Dual N-Back pre-degree vs. BSL Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. BSL Grades 2nd year", 
       y = "2nd year BSL grade", x = "Dual N-Back pre-degree")

# correlation
apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0101
# Dual N-Back 2nd year vs. BSL Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. BSL Grades 2nd year", 
       y = "2nd year BSL grade", x = "Dual N-Back 2nd year")

# correlation
apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0520
vs BSL-SRT

Does Dual N-Back performance relate to BSL Sentence Reproduction Task?

# Initial Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Initial Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "Initial Dual N-Back score")

# correlation
apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00186
# 2nd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "2nd year Dual N-Back score")

# correlation
apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.218
# 3rd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "3rd year Dual N-Back score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", 
       x = "3rd year Dual N-Back score")

# correlation
apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.122
# linear model
lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3928 -0.6371 -0.3928  0.6072  6.9485 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)           -19.9281    38.7294  -0.515    0.625
## `nback_comb_3rd year`  46.3473    56.0801   0.826    0.440
## self_rating            -0.1952     1.4719  -0.133    0.899
## 
## Residual standard error: 3.82 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.1246, Adjusted R-squared:  -0.1671 
## F-statistic: 0.4272 on 2 and 6 DF,  p-value: 0.6707

Digit Span

vs BSL Grades
# Digit Span pre-degree vs. BSL grades 1st year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "Digit Span pre-degree")

# correlation
apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.122
# Digit Span pre-degree vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Digit Span pre-degree")

# correlation
apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.141
# No datapoints for Digit Span @ '1 year of study'

# Digit Span 2nd year vs. BSL grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "Digit Span 2nd year")

# correlation
apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0796
vs BSL-SRT

Does Digit Span score relate to BSL-SRT score?

# Initial Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial Digit Span score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "Initial Digit Span score")

# correlation
apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00124
# 2nd year Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year Digit Span score vs. 3rd year BSL-SRT scores", 
       y = "3rd year BSL-SRT score", x = "2nd year Digit Span score")

# correlation
apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00433

Barratt Impulsiveness Scale

vs BSL Grades
# Barratt Impulsiveness Scale 2nd year vs BSL grades 1st year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_bsl_1st year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity vs. BSL grades 1st year", 
       y = "1st year BSL grade", x = "BIS 2nd year")

# correlation
apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0468
# Barratt Impulsiveness Scale 2nd year vs BSL grades 2nd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_bsl_2nd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity vs. BSL grades 2nd year", 
       y = "2nd year BSL grade", x = "BIS 2nd year")

# correlation
apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00858
# Barratt Impulsiveness Scale 2nd year vs BSL self-rated proficiency
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(self_rating != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = self_rating)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity vs. Initial BSL self-rating", 
       y = "Initial BSL self-rating", x = "BIS 2nd year")

vs BSL-SRT
# Barratt Impulsiveness Scale 2nd year vs BSL-SRT 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`bsl_srt_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. BSL-SRT 3rd year", 
       y = "3rd year BSL-SRT score", x = "BIS Score 2nd year")

# correlation
apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.272
# linear model
lm(`bsl_srt_3rd year` ~ `bis_tot_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `bis_tot_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7743 -1.2297  0.4997  2.5887  4.0756 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         25.0529     9.3110   2.691    0.036 *
## `bis_tot_2nd year`  -0.1815     0.1248  -1.454    0.196  
## self_rating         -0.2987     1.2260  -0.244    0.816  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.466 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.2791, Adjusted R-squared:  0.03883 
## F-statistic: 1.162 on 2 and 6 DF,  p-value: 0.3746

Interpreting: predicted impact

Now we turn to look at the predictor variables which we hypothesised may have a relationship with SLI outcomes.

Dual N-Back

vs Interpreting Grades
# Dual N-Back pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Dual N-Back pre-degree")

# correlation
apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0123
# Dual N-Back pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Dual N-Back pre-degree")

# correlation
apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0992
# Dual N-Back 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Dual N-Back 2nd year")

# correlation
apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000705
vs Eng>BSL interpreting
# Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back pre-degree")

# correlation
apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.125
# Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 2nd year")

# correlation
apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.121
# Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 3rd year")

# correlation
apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.155
# linear model
lm(`terp_e2b_3rd year` ~ `nback_comb_3rd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.3649  -5.7844  -1.9770   0.6793  20.5151 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -38.624    133.735  -0.289    0.782
## `nback_comb_3rd year`  136.419    193.649   0.704    0.508
## self_rating              1.444      5.083   0.284    0.786
## 
## Residual standard error: 13.19 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.1663, Adjusted R-squared:  -0.1117 
## F-statistic: 0.5982 on 2 and 6 DF,  p-value: 0.5796
vs BSL>Eng interpreting
# Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.71, .75)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back pre-degree")

# correlation
apt_wide %>% 
  filter(`nback_comb_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`nback_comb_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0605
# Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 2nd year")

# correlation
apt_wide %>% 
  filter(`nback_comb_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.178
# Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `nback_comb_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = " Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 3rd year")

# correlation
apt_wide %>% 
  filter(`nback_comb_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`nback_comb_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.103

Corsi Blocks

vs Interpreting Grades
# Corsi Blocks pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(8, 10)) +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Corsi Blocks pre-degree")

# correlation
apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0114
# Corsi Blocks pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks pre-degree")

# correlation
apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00378
# Corsi Blocks 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_1st year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks 1st year")

# correlation
apt_wide %>% 
  filter(`corsi_corr_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.164
# Corsi Blocks 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(7, 10)) +
  theme_minimal() + theme_details + 
  labs(title = "Corsi Blocks 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Corsi Blocks 2nd year")

# correlation
apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0791
vs Eng>BSL interpreting
#  Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(6, 10)) +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks pre-degree")

# correlation
apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.289
# linear model
lm(`terp_e2b_3rd year` ~ `corsi_corr_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.9254  -8.4322  -0.1853   1.9101  22.9235 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)             19.58284   29.41334   0.666     0.53
## `corsi_corr_pre-degree`  5.07783    3.98831   1.273     0.25
## self_rating             -0.06448    4.75634  -0.014     0.99
## 
## Residual standard error: 12.18 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.2893, Adjusted R-squared:  0.0524 
## F-statistic: 1.221 on 2 and 6 DF,  p-value: 0.359
#  Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year
( fig7a <- apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(7, 11)) +
  theme_minimal() + theme_details + 
  labs(title = "a) 2nd year Visuospatial WM vs. 3rd yr Eng>BSL interp.",
       y = "3rd year Eng>BSL interp. score", 
       x = "2nd year Corsi Blocks score") )

# correlation
apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.565
# linear model
lm(`terp_e2b_3rd year` ~ `corsi_corr_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     13     15     17     18     20     21     22     26 
## -8.536 -1.520  2.139 -6.065 -9.681  3.519  8.615 11.529 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             1.3786    23.2375   0.059   0.9550  
## `corsi_corr_2nd year`   6.5196     2.6245   2.484   0.0556 .
## self_rating             0.6451     3.1611   0.204   0.8463  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.271 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.5687, Adjusted R-squared:  0.3962 
## F-statistic: 3.297 on 2 and 5 DF,  p-value: 0.1222
#  Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks 3rd yr vs. Eng to BSL interpreting 3rd yr",
       y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 3rd year")

# correlation
apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.132
vs BSL>Eng interpreting
#  Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(6, 10)) +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks pre-degree")

# correlation
apt_wide %>% 
  filter(`corsi_corr_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`corsi_corr_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0578
#  Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 2nd year")

# correlation
apt_wide %>% 
  filter(`corsi_corr_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.455
# linear model
lm(`terp_b2e_3rd year` ~ `corsi_corr_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `corsi_corr_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22 
## -0.3476 -0.3357  4.8847 -2.7752 -3.9221 -1.3221  3.8180 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            45.2283    10.4934   4.310   0.0125 *
## `corsi_corr_2nd year`   2.2068     1.2131   1.819   0.1430  
## self_rating            -0.3604     1.5418  -0.234   0.8266  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.984 on 4 degrees of freedom
##   (23 observations deleted due to missingness)
## Multiple R-squared:  0.4624, Adjusted R-squared:  0.1937 
## F-statistic: 1.721 on 2 and 4 DF,  p-value: 0.289
#  Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = " Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 3rd year")

# correlation
apt_wide %>% 
  filter(`corsi_corr_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`corsi_corr_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0169

Digit Span

vs Interpreting Grades
# Digit Span pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.6, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Digit Span pre-degree")

# correlation
apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0155
# Digit Span pre-degree vs. Interpreting Grades 2nd year
( fig7b <- apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  coord_cartesian(xlim = c(.51, .87)) +  
  theme_minimal() + theme_details + 
  labs(title = "b) Initial Auditory WM vs. 2nd year Interpreting Grade", 
       y = "2nd year Interpreting Grade", 
       x = "Initial Digit Span Accuracy") )

# correlation
apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.338
# linear model
lm(`grade_terp_2nd year` ~ `dspan_corr_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.043  -3.731   2.012   5.848   8.327 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               13.232     18.749   0.706   0.4965  
## `dspan_corr_pre-degree`   58.906     23.551   2.501   0.0314 *
## self_rating                2.033      2.088   0.973   0.3534  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.528 on 10 degrees of freedom
##   (17 observations deleted due to missingness)
## Multiple R-squared:  0.3954, Adjusted R-squared:  0.2745 
## F-statistic:  3.27 on 2 and 10 DF,  p-value: 0.08079
# Digit Span 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Digit Span pre-degree")

# correlation
apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0709
vs Eng>BSL interpreting
# Digit Span pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span pre-degree")

# correlation
apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0926
# Digit Span 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .81)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span 2nd year")

# correlation
apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0851
# Digit Span 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.7, .86)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Digit Span 3rd year")

# correlation
apt_wide %>% 
  filter(`dspan_corr_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.115
vs BSL>Eng interpreting
# Digit Span pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.63, .72)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span pre-degree")

# correlation
apt_wide %>% 
  filter(`dspan_corr_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`dspan_corr_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00697
# Digit Span 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") + 
  coord_cartesian(xlim = c(.63, .79)) +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span 2nd year")

# correlation
apt_wide %>% 
  filter(`dspan_corr_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0863
# Digit Span 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Digit Span 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Digit Span 3rd year")

# correlation
apt_wide %>% 
  filter(`dspan_corr_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`dspan_corr_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0264

Combine plots for Figure 7 using patchwork:

( fig7 <- (fig7a | fig7b) )

ggsave("plots/fig7.jpeg", plot=fig7, width = 12, height = 5, units = "in")

Summarising Task

vs Interpreting Grades
# Summarising pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Summarising pre-degree")

# correlation
apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0334
vs Eng>BSL interpreting
# Summarising pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. English to BSL interpreting 3rd year", 
       y = "English to BSL interpreting 3rd year", x = "Summarising pre-degree")

# correlation
apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0447
vs BSL>Eng interpreting
# Summarising pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `summ_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Summarising pre-degree vs. BSL to English interpreting 3rd year", 
       y = "BSL to English interpreting 3rd year", x = "Summarising pre-degree")

# correlation
apt_wide %>% 
  filter(`summ_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`summ_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00303

Interpreting: no predicted impact

Now we examine the predictor variables where we hypothesised there was unlikely to be a relationship with SLI outcomes.

Kirklees Sentence Reading

vs Interpreting Grades
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.48, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")

# correlation
apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.160
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.56, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")

# correlation
apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0883
# Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_1st year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading after 1 year")

# correlation
apt_wide %>% 
  filter(`kirk_acc_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0201
# Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading 2nd year")

# correlation
apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0457
vs Eng>BSL interpreting
# Kirklees pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.56, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Kirklees pre-degree")

# correlation
apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0754
# Kirklees 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "Kirklees 2nd year")

# correlation
apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0193
vs BSL>Eng interpreting
# Kirklees pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.56, .93)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Kirklees pre-degree")

# correlation
apt_wide %>% 
  filter(`kirk_acc_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kirk_acc_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0922
# Kirklees 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "Kirklees 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "Kirklees 2nd year")

# correlation
apt_wide %>% 
  filter(`kirk_acc_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kirk_acc_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000130

KBIT-2 Matrices

vs Interpreting Grades
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.82, .95)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "KBIT-2 Matrices pre-degree")

# correlation
apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0419
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices pre-degree")

# correlation
apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0232
# KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_1st year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 1st year")

# correlation
apt_wide %>% 
  filter(`kbit_acc_1st year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00117
# KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 2nd year")

# correlation
apt_wide %>% 
  filter(`kbit_acc_2nd year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.116
vs Eng>BSL interpreting
# KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.84, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")

# correlation
apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000458
# KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")

# correlation
apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##        RSq
##      <dbl>
## 1 0.000597
vs BSL>Eng interpreting
# KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(.84, 1)) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")

# correlation
apt_wide %>% 
  filter(`kbit_acc_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`kbit_acc_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0417
# KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
  theme_minimal() + theme_details + 
  labs(title = "KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")

# correlation
apt_wide %>% 
  filter(`kbit_acc_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`kbit_acc_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0172

2D Mental Rotation

vs Interpreting Grades
# 2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_point(size = 2) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "2D Mental Rotation pre-degree")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0129
# 2D Mental Rotation 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-1.5, 1.5)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "2D Mental Rotation 1st year")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_2nd year` != "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.150
lm(`grade_terp_2nd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -14.991  -5.482  -2.225   7.289  12.136 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            56.268      7.004   8.033 8.88e-05 ***
## `mr2d_sats_2nd year`    3.728      3.273   1.139    0.292    
## self_rating             1.610      2.987   0.539    0.607    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.86 on 7 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.1841, Adjusted R-squared:  -0.04897 
## F-statistic: 0.7899 on 2 and 7 DF,  p-value: 0.4905
# 2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-.5, 1)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 3rd year vs. Interpreting Grades 2nd year", 
       y = "3rd year Interpreting grade", x = "2D Mental Rotation 2nd year")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.179
# linear model
lm(`grade_terp_2nd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      26      28 
##   5.237   1.013  -6.849  -1.215   1.955   7.107 -11.393   4.146 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            48.522      6.334   7.661 0.000604 ***
## `mr2d_sats_3rd year`   10.531      5.893   1.787 0.133989    
## self_rating             3.986      2.584   1.543 0.183573    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.458 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.4436, Adjusted R-squared:  0.221 
## F-statistic: 1.993 on 2 and 5 DF,  p-value: 0.2309
vs Eng>BSL interpreting
# 2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 2nd year")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.198
# linear model
lm(`terp_e2b_3rd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
## -12.4203   3.6479 -13.5813   8.6741  -5.9900  -0.5966  11.9458   8.3205 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            54.374      8.536   6.370  0.00141 **
## `mr2d_sats_2nd year`    6.633      4.569   1.452  0.20624   
## self_rating             3.948      4.121   0.958  0.38199   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.62 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.3222, Adjusted R-squared:  0.05108 
## F-statistic: 1.188 on 2 and 5 DF,  p-value: 0.3782
# 2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 3rd year")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.180
# linear model
lm(`terp_e2b_3rd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.6489  -6.8092   0.7963   8.7048  10.6778 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)            47.221      9.204   5.131  0.00216 **
## `mr2d_sats_3rd year`   11.842      6.718   1.763  0.12840   
## self_rating             5.611      3.722   1.507  0.18245   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.14 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.4053, Adjusted R-squared:  0.2071 
## F-statistic: 2.045 on 2 and 6 DF,  p-value: 0.2103
vs BSL>Eng interpreting
# 2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
( fig8a <- apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  coord_cartesian(xlim = c(-1.3, 1.3)) +
  theme_minimal() + theme_details + 
  labs(title = "a) 2nd year 2D Mental Rotation vs. 3rd year BSL>Eng interp.",
       y = "3rd year BSL>Eng interpreting", x = "2nd year 2D Mental Rotation") )

# correlation
apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.602
# linear model
lm(`terp_b2e_3rd year` ~ `mr2d_sats_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22 
## -2.1612  0.6959 -1.4352  3.7724 -0.9446 -2.6385  2.7113 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            62.648      2.243  27.933 9.77e-06 ***
## `mr2d_sats_2nd year`    3.648      1.239   2.944   0.0422 *  
## self_rating             1.205      1.134   1.062   0.3479    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.027 on 4 degrees of freedom
##   (23 observations deleted due to missingness)
## Multiple R-squared:  0.6898, Adjusted R-squared:  0.5346 
## F-statistic: 4.446 on 2 and 4 DF,  p-value: 0.09625
# 2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
( fig8b <- apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  coord_cartesian(xlim = c(-0.5, 1.4)) +
  theme_minimal() + theme_details + 
  labs(title = "b) 3rd yr 2D Mental Rotation vs. 3rd yr BSL>Eng interp.",
       y = "3rd year BSL>Eng interpreting", x = "3rd year 2D Mental Rotation") )

# correlation
apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.311
# linear model
lm(`terp_b2e_3rd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
## -3.0760  2.8403 -0.2463  2.5299 -1.6912 -5.1393  2.8670  1.9156 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            60.032      3.008  19.955 5.84e-06 ***
## `mr2d_sats_3rd year`    4.599      2.177   2.112   0.0884 .  
## self_rating             1.772      1.276   1.389   0.2235    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.61 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.5025, Adjusted R-squared:  0.3035 
## F-statistic: 2.525 on 2 and 5 DF,  p-value: 0.1746

3D Mental Rotation

vs Interpreting Grades
# 3D Mental Rotation pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-2, 1.5)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation pre-degree vs. Interpreting Grades 1st year", 
       y = "1st year Interpreting grade", x = "3D Mental Rotation pre-degree")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_1st year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_terp_1st year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0677
# 3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  geom_point(size = 2) +
  theme_minimal() + theme_details + 
  labs(title= "3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "3D Mental Rotation pre-degree") 

# correlation
apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.182
# 3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title= "3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
       y = "2nd year Interpreting grade", x = "3D Mental Rotation 2nd year") 

# correlation
apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.261
# 3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title= "3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
       y = "2nd year Interpreting grade", x = "3D Mental Rotation 3rd year") 

# correlation
apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.435
# linear model
lm(`grade_terp_2nd year` ~ `mr3d_sats_3rd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr3d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      26      28 
## -1.4232  0.2050 -8.6905  0.3706 -2.2990  9.8876 -4.3629  6.3124 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            49.449      5.537   8.931 0.000293 ***
## `mr3d_sats_3rd year`    5.877      2.768   2.123 0.087131 .  
## self_rating             2.071      2.189   0.946 0.387719    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.923 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.5206, Adjusted R-squared:  0.3288 
## F-statistic: 2.714 on 2 and 5 DF,  p-value: 0.1592
vs Eng>BSL interpreting
# 3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year
( fig8c <- apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-2.7, 1.1)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "c) Initial 3D Mental Rotation vs. 3rd yr Eng>BSL interp.",
       y = "3rd year Eng>BSL interpreting", x = "Initial 3D Mental Rotation") )

# correlation
apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.268
# linear model
lm(`terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.243  -5.679   1.404   2.243  18.096 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              62.273     10.114   6.157 0.000842 ***
## `mr3d_sats_pre-degree`    5.158      3.917   1.317 0.235955    
## self_rating               2.062      3.973   0.519 0.622317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.09 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.2997, Adjusted R-squared:  0.06625 
## F-statistic: 1.284 on 2 and 6 DF,  p-value: 0.3435
# 3D Mental Rotation 2nd year vs. Eng to BSL interpreting  3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-1.2, 2.2)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 2nd year")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0974
# 3D Mental Rotation 3rd year vs. Eng to BSL interpreting  3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
       y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 3rd year")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##       RSq
##     <dbl>
## 1 0.00752
vs BSL>Eng interpreting
# 3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year
( fig8d <- apt_wide %>%
  ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-2.7, 1.2)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "d) Initial 3D Mental Rotation vs. 3rd yr BSL>Eng interp.",
       y = "3rd year BSL>Eng interpreting", x = "Initial 3D Mental Rotation") )

# correlation
apt_wide %>% 
  filter(`mr3d_sats_pre-degree`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_pre-degree`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.475
# linear model
lm(`terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
## -4.4451  0.3171  0.6291 -0.3183 -1.4933 -2.9145  2.7963  5.4287 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             66.4684     3.1264  21.260 4.27e-06 ***
## `mr3d_sats_pre-degree`   2.4042     1.1997   2.004    0.101    
## self_rating              0.2235     1.2903   0.173    0.869    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.698 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.4779, Adjusted R-squared:  0.2691 
## F-statistic: 2.289 on 2 and 5 DF,  p-value: 0.1969
# 3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(-1.2, 2.2)) +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.286
# 3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
  theme_minimal() + theme_details + 
  labs(title = "3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
       y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")

# correlation
apt_wide %>% 
  filter(`mr3d_sats_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>%   
  summarize(RSq=(cor(`mr3d_sats_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.158

Combine plots for Figure 8 using patchwork:

( fig8 <- (fig8a | fig8b)/(fig8c | fig8d) )

ggsave("plots/Fig8.jpeg", plot=fig8, width = 12, height = 8, units = "in")

Barratt Impulsiveness Scale

vs Interpreting Grades
# Barratt Impulsiveness Scale 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`grade_terp_2nd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. Interpreting Grades 2nd year", 
       y = "2nd year Interpreting grade", x = "BIS score 2nd year")

# correlation
apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##         RSq
##       <dbl>
## 1 0.0000311
vs Eng>BSL interpreting
# Barratt Impulsiveness Scale 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`terp_e2b_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. Eng to BSL interpreting 3rd year", 
       y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")

# correlation
apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`terp_e2b_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.115
vs BSL>Eng interpreting
# Barratt Impulsiveness Scale 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
  filter(`bis_tot_2nd year` != "na") %>% 
  filter(`terp_b2e_3rd year` != "na") %>% 
  ggplot(aes(x = `bis_tot_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Impulsivity 2nd year vs. BSL to Eng interpreting 3rd year", 
       y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")

# correlation
apt_wide %>% 
  filter(`bis_tot_2nd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`bis_tot_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.236

Correlations among predictor variables

Here we look at the relationships among predictor variables to see if they correlate

# Do 2D- and 3D-MR correlate in second year?
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_2nd year`, y = `mr3d_sats_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "2nd year 2D Mental Rotation vs. 2nd year 3D Mental Rotation", 
       y = "2nd year 2D Mental Rotation", x = "2nd year 3D Mental Rotation")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_2nd year`!= "NA") %>% 
  filter(`mr3d_sats_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_2nd year`, 
                       `mr3d_sats_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.304
# linear model
lm(`mr2d_sats_2nd year` ~ `mr3d_sats_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `mr2d_sats_2nd year` ~ `mr3d_sats_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6104 -0.5109  0.1422  0.5531  1.0200 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.4178     0.6252   0.668   0.5228  
## `mr3d_sats_2nd year`   0.6131     0.2556   2.399   0.0433 *
## self_rating           -0.3866     0.3076  -1.257   0.2442  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8792 on 8 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.4185, Adjusted R-squared:  0.2732 
## F-statistic: 2.879 on 2 and 8 DF,  p-value: 0.1143
# Do 2D- and 3D-MR correlate in third year?
apt_wide %>%
  ggplot(aes(x = `mr2d_sats_3rd year`, y = `mr3d_sats_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "3rd year 2D Mental Rotation vs. 3rd year 3D Mental Rotation", 
       y = "3rd year 2D Mental Rotation", x = "3rd year 3D Mental Rotation")

# correlation
apt_wide %>% 
  filter(`mr2d_sats_3rd year`!= "NA") %>% 
  filter(`mr3d_sats_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`mr2d_sats_3rd year`, 
                       `mr3d_sats_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.491
# linear model
lm(`mr3d_sats_3rd year` ~ `mr2d_sats_3rd year` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `mr3d_sats_3rd year` ~ `mr2d_sats_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0954 -0.2439 -0.1139  0.3497  1.1442 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.1346     0.6280   0.214   0.8374  
## `mr2d_sats_3rd year`   1.2452     0.4584   2.717   0.0348 *
## self_rating            0.2280     0.2540   0.898   0.4039  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.76 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.5516, Adjusted R-squared:  0.4021 
## F-statistic:  3.69 on 2 and 6 DF,  p-value: 0.09017
# significantly correlate with each other

# Does initial copy-sign correlate with initial BSL self-ratings?
apt_wide %>%
  ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "Initial copy-sign score vs. 1st year BSL grade", 
       y = "Initial copy-sign score", x = "1st year BSL grade")

# correlation
apt_wide %>% 
  filter(`copy_sign_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`copy_sign_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.459
# linear model
lm(`grade_bsl_1st year` ~ `copy_sign_pre-degree` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `copy_sign_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.3659  -1.3727   0.8047   3.6029   6.4859 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             22.1538    17.2326   1.286   0.2346  
## `copy_sign_pre-degree`   0.5650     0.2509   2.252   0.0544 .
## self_rating             -0.6055     1.9452  -0.311   0.7635  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.641 on 8 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.4658, Adjusted R-squared:  0.3322 
## F-statistic: 3.487 on 2 and 8 DF,  p-value: 0.08147
# marginally insignificant

Correlations among outcome variables

Now we look at relationships among outcome variables to see if they correlate

# Does interpreting Eng>BSL correlate with interpreting BSL>Eng?
apt_wide %>%
  ggplot(aes(x = `terp_e2b_3rd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "3rd year English-to-BSL interpreting vs. 3rd year BSL-to-English interpreting", 
       y = "3rd year BSL-to-English interpreting", x = "3rd year English-to-BSL interpreting")

# correlation
apt_wide %>% 
  filter(`terp_e2b_3rd year`!= "NA") %>% 
  filter(`terp_b2e_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`terp_e2b_3rd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.788
# linear model
lm(`terp_b2e_3rd year` ~ `terp_e2b_3rd year` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `terp_e2b_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
##  0.6655  0.4638  3.6894 -1.1593 -1.3687 -2.6088  1.4733 -1.1552 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         47.06528    4.25053  11.073 0.000105 ***
## `terp_e2b_3rd year`  0.29092    0.07020   4.144 0.008959 ** 
## self_rating         -0.04233    0.82549  -0.051 0.961090    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.358 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.7877, Adjusted R-squared:  0.7028 
## F-statistic: 9.278 on 2 and 5 DF,  p-value: 0.02076
# significant predictor

# Does interpreting grades correlate with interpreting Eng>BSL?
apt_wide %>%
  ggplot(aes(x = `grade_terp_2nd year`, y = `terp_e2b_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +  
  coord_cartesian(xlim = c(50, 70)) +
  theme_minimal() + theme_details + 
  labs(title= "2nd year interpreting grade vs. 3rd year English-to-BSL interpreting", 
       y = "3rd year English-to-BSL interpreting", x = "2nd year interpreting grade")

# correlation
apt_wide %>% 
  filter(`terp_e2b_3rd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`grade_terp_2nd year`, 
                       `terp_e2b_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0640
# linear model
lm(`terp_e2b_3rd year` ~ `grade_terp_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `grade_terp_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      26      28 
## -13.582   9.169  -5.995   3.637 -10.743  -1.039   5.549  13.005 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            42.7561    29.8472   1.433    0.211
## `grade_terp_2nd year`   0.1840     0.5275   0.349    0.741
## self_rating             3.1036     3.7296   0.832    0.443
## 
## Residual standard error: 11.26 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1779, Adjusted R-squared:  -0.1509 
## F-statistic: 0.541 on 2 and 5 DF,  p-value: 0.6128
# Does interpreting grades correlate with interpreting BSL>Eng?
apt_wide %>%
  ggplot(aes(x = `grade_terp_2nd year`, y = `terp_b2e_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(50, 70)) +
  theme_minimal() + theme_details + 
  labs(title= "2nd year interpreting grade vs. 3rd year BSL-to-English interpreting", 
       y = "3rd year BSL-to-English interpreting", x = "2nd year interpreting grade")

# correlation
apt_wide %>% 
  filter(`terp_b2e_3rd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`grade_terp_2nd year`, 
                       `terp_b2e_3rd year`))^2)
## # A tibble: 1 x 1
##      RSq
##    <dbl>
## 1 0.0426
# linear model
lm(`terp_b2e_3rd year` ~ `grade_terp_2nd year` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `grade_terp_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      28 
## -2.3229  3.3993  2.1240  0.1452 -4.3825 -2.3748  3.4118 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           60.68928   11.48602   5.284  0.00615 **
## `grade_terp_2nd year`  0.03117    0.20838   0.150  0.88833   
## self_rating            0.70349    1.45773   0.483  0.65461   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.807 on 4 degrees of freedom
##   (23 observations deleted due to missingness)
## Multiple R-squared:  0.09532,    Adjusted R-squared:  -0.357 
## F-statistic: 0.2107 on 2 and 4 DF,  p-value: 0.8184
# Do interpreting grades correlate with BSL grades?
apt_wide %>%
  ggplot(aes(x = `grade_terp_2nd year`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title= "2nd year interpreting grade vs. 2nd year BSL grade", 
       y = "2nd year BSL grade", x = "2nd year interpreting grade")

# correlation
apt_wide %>% 
  filter(`grade_bsl_2nd year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`grade_bsl_2nd year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.685
# linear model
lm(`grade_bsl_2nd year` ~ `grade_terp_2nd year` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `grade_terp_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.5858  -2.1350  -0.2428   1.7075  10.6495 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            15.3815     6.9110   2.226   0.0371 *  
## `grade_terp_2nd year`   0.7753     0.1142   6.787 1.03e-06 ***
## self_rating             0.7872     0.9090   0.866   0.3963    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.013 on 21 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.6963, Adjusted R-squared:  0.6673 
## F-statistic: 24.07 on 2 and 21 DF,  p-value: 3.682e-06
# significantly correlate

# Do 1st year BSL grades correlate with 2nd year interpreting grades?
apt_wide %>%
  ggplot(aes(x = `grade_bsl_1st year`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(56, 79)) +
  theme_minimal() + theme_details + 
  labs(title= "2nd year interpreting grade vs. 1st year BSL grade", 
       y = "2nd year interpreting grade", x = "1st year BSL grade")

# correlation
apt_wide %>% 
  filter(`grade_bsl_1st year`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`grade_bsl_1st year`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.637
# linear model
lm(`grade_terp_2nd year` ~ `grade_bsl_1st year` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `grade_bsl_1st year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.805 -3.752  0.048  2.102 14.080 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -12.0606    11.0658  -1.090    0.288    
## `grade_bsl_1st year`   1.1474     0.1784   6.433 2.24e-06 ***
## self_rating           -1.4193     1.0551  -1.345    0.193    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.556 on 21 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.6658, Adjusted R-squared:  0.6339 
## F-statistic: 20.92 on 2 and 21 DF,  p-value: 1.006e-05
# significantly correlate

# Does initial copy-sign correlate with 1st-year BSL grades?
apt_wide %>%
  ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_bsl_1st year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  theme_minimal() + theme_details + 
  labs(title = "Initial copy-sign score vs. 1st year BSL grade", 
       y = "1st year BSL grade", x = "Initial copy-sign score")

# correlation
apt_wide %>% 
  filter(`copy_sign_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_1st year` != "NA") %>% 
  summarize(RSq=(cor(`copy_sign_pre-degree`, 
                       `grade_bsl_1st year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.459
# linear model
lm(`grade_bsl_1st year` ~ `copy_sign_pre-degree` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `grade_bsl_1st year` ~ `copy_sign_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.3659  -1.3727   0.8047   3.6029   6.4859 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)             22.1538    17.2326   1.286   0.2346  
## `copy_sign_pre-degree`   0.5650     0.2509   2.252   0.0544 .
## self_rating             -0.6055     1.9452  -0.311   0.7635  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.641 on 8 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.4658, Adjusted R-squared:  0.3322 
## F-statistic: 3.487 on 2 and 8 DF,  p-value: 0.08147
# interestingly, no relationship

# Does initial copy-sign correlate with 2nd-year BSL grades?
apt_wide %>%
  ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_bsl_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") + 
  theme_minimal() + theme_details + 
  labs(title = "Initial copy-sign score vs. 2nd year BSL grade", 
       y = "2nd year BSL grade", x = "Initial copy-sign score")

# correlation
apt_wide %>% 
  filter(`copy_sign_pre-degree`!= "NA") %>% 
  filter(`grade_bsl_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`copy_sign_pre-degree`, 
                       `grade_bsl_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.210
# linear model
lm(`grade_bsl_2nd year` ~ `copy_sign_pre-degree` +
                   self_rating, data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `copy_sign_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.9987  -2.5943  -0.9078   5.5478   8.5903 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)            31.97137   28.04795   1.140    0.298
## `copy_sign_pre-degree`  0.36337    0.43065   0.844    0.431
## self_rating             0.04689    3.18968   0.015    0.989
## 
## Residual standard error: 7.48 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.2099, Adjusted R-squared:  -0.05349 
## F-statistic: 0.7969 on 2 and 6 DF,  p-value: 0.4933
# no relationship

# Does initial copy-sign correlate with later terp grades?
apt_wide %>%
  ggplot(aes(x = `copy_sign_pre-degree`, y = `grade_terp_2nd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm")  +
  theme_minimal() + theme_details + 
  labs(title = "Initial copy-sign score vs. 2nd year interpreting grade", 
       y = "2nd year interpreting grade", x = "Initial copy-sign score")

# correlation
apt_wide %>% 
  filter(`copy_sign_pre-degree`!= "NA") %>% 
  filter(`grade_terp_2nd year` != "NA") %>% 
  summarize(RSq=(cor(`copy_sign_pre-degree`, 
                       `grade_terp_2nd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.217
# linear model
lm(`grade_terp_2nd year` ~ `copy_sign_pre-degree` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `copy_sign_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.6507 -4.9029 -0.2995  5.9080  8.9164 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)            28.83191   27.37127   1.053    0.333
## `copy_sign_pre-degree`  0.36033    0.42026   0.857    0.424
## self_rating             0.06346    3.11272   0.020    0.984
## 
## Residual standard error: 7.3 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.2168, Adjusted R-squared:  -0.04423 
## F-statistic: 0.8306 on 2 and 6 DF,  p-value: 0.4804
# interestingly yes (marginal)

# does initial copy-sign correlate with 3rd year BSL-SRT?
apt_wide %>%
  ggplot(aes(x = `copy_sign_pre-degree`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(70, 90)) +
  theme_minimal() + theme_details + 
  labs(title = "Initial copy-sign score vs. 3rd year BSL-SRT score", 
       y = "3rd year BSL-SRT score", x = "Initial copy-sign score")

# correlation
apt_wide %>% 
  filter(`copy_sign_pre-degree`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`copy_sign_pre-degree`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.513
# linear model
lm(`bsl_srt_3rd year` ~ `copy_sign_pre-degree` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `copy_sign_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     13     17     18     22     26     28 
##  1.636 -2.239  2.735  3.379 -2.915 -2.596 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -19.3673    17.5120  -1.106    0.349
## `copy_sign_pre-degree`   0.4203     0.2441   1.722    0.184
## self_rating             -0.8146     1.9695  -0.414    0.707
## 
## Residual standard error: 3.734 on 3 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.5396, Adjusted R-squared:  0.2326 
## F-statistic: 1.758 on 2 and 3 DF,  p-value: 0.3124
# do 1st year BSL grades correlate with 3rd year BSL-SRT?
apt_wide %>%
  ggplot(aes(x = `grade_bsl_1st year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(56, 78)) +
  theme_minimal() + theme_details + 
  labs(title = "1st year BSL grade vs. 3rd year BSL-SRT score", 
       y = "3rd year BSL-SRT score", x = "1st year BSL grade")

# correlation
apt_wide %>% 
  filter(`grade_bsl_1st year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`grade_bsl_1st year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.661
# linear model
lm(`bsl_srt_3rd year` ~ `grade_bsl_1st year` +
                   self_rating, data = apt_wide) %>% summary() # significant predictor 
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `grade_bsl_1st year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7767 -0.8431  0.2478  1.2318  2.4714 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          -11.0353     7.0436  -1.567   0.1682  
## `grade_bsl_1st year`   0.3719     0.1104   3.369   0.0151 *
## self_rating           -0.1323     0.7727  -0.171   0.8697  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.37 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.6629, Adjusted R-squared:  0.5505 
## F-statistic: 5.899 on 2 and 6 DF,  p-value: 0.03832
# do 2nd-year BSL grades correlate with 3rd year BSL-SRT?
apt_wide %>%
  ggplot(aes(x = `grade_bsl_2nd year`, y = `bsl_srt_3rd year`)) +
  geom_point(size = 2) +
  geom_smooth(method= "lm") +
  coord_cartesian(xlim = c(50, 71)) +
  theme_minimal() + theme_details + 
  labs(title = "2nd year BSL grade vs. 3rd year BSL-SRT score", 
       y = "3rd year BSL-SRT score", x = "2nd year BSL grade")

# correlation
apt_wide %>% 
  filter(`grade_bsl_2nd year`!= "NA") %>% 
  filter(`bsl_srt_3rd year` != "NA") %>% 
  summarize(RSq=(cor(`grade_bsl_2nd year`, 
                       `bsl_srt_3rd year`))^2)
## # A tibble: 1 x 1
##     RSq
##   <dbl>
## 1 0.526
# linear model
lm(`bsl_srt_3rd year` ~ `grade_bsl_2nd year` +
                   self_rating, data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `grade_bsl_2nd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      26      28 
## -0.4133  0.6953 -3.6954  0.7176  0.7394  1.6953  1.3050 -1.0438 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          -1.63326    5.85746  -0.279   0.7915  
## `grade_bsl_2nd year`  0.21744    0.09656   2.252   0.0741 .
## self_rating           0.15210    0.66682   0.228   0.8286  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.051 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.5312, Adjusted R-squared:  0.3436 
## F-statistic: 2.832 on 2 and 5 DF,  p-value: 0.1505
# marginally insignificant

Models

The below combined models are not reported in the manuscript because they are being fit on too few observations

BSL Grades

What best predicts BSL grades?

# wide version - pre-degree
lm(`grade_bsl_2nd year` ~ `nback_comb_pre-degree` +
     `mr3d_sats_pre-degree` + 
     `corsi_corr_pre-degree` +
     `kirk_acc_pre-degree` + 
     #`mlat_acc_pre-degree` +
     self_rating +
     #`copy_sign_pre-degree`
     `grade_bsl_1st year`,
   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     self_rating + `grade_bsl_1st year`, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.8502 -3.1710  0.2518  3.3076 12.9512 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -21.6891    37.0515  -0.585    0.571    
## `nback_comb_pre-degree`  14.9884    41.4362   0.362    0.725    
## `mr3d_sats_pre-degree`   -1.4740     1.1047  -1.334    0.212    
## `corsi_corr_pre-degree`  -0.6435     1.5553  -0.414    0.688    
## `kirk_acc_pre-degree`    -9.5188    18.9609  -0.502    0.627    
## self_rating              -0.7772     1.7167  -0.453    0.660    
## `grade_bsl_1st year`      1.3569     0.2075   6.540 6.55e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.248 on 10 degrees of freedom
##   (13 observations deleted due to missingness)
## Multiple R-squared:  0.8255, Adjusted R-squared:  0.7209 
## F-statistic: 7.887 on 6 and 10 DF,  p-value: 0.002481
# final assessments in 3rd year
lm(`grade_bsl_2nd year` ~ `nback_comb_3rd year` + 
     `mr3d_sats_3rd year` + 
     `mr2d_sats_3rd year` +
     `corsi_corr_3rd year` +
     `dspan_corr_3rd year` +
     self_rating,
   data = apt_wide) %>% summary() 
## 
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      26      28 
##  0.9065 -0.3100  0.5909  4.0307 -3.2833  1.4403 -2.0927 -1.2824 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -66.525     71.800  -0.927    0.524
## `nback_comb_3rd year`  144.872    138.270   1.048    0.485
## `mr3d_sats_3rd year`     5.334      3.392   1.573    0.361
## `mr2d_sats_3rd year`     3.461      8.506   0.407    0.754
## `corsi_corr_3rd year`    6.253      2.398   2.608    0.233
## `dspan_corr_3rd year`  -44.825    101.233  -0.443    0.735
## self_rating             -1.854      3.168  -0.585    0.663
## 
## Residual standard error: 6.033 on 1 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.9237, Adjusted R-squared:  0.4658 
## F-statistic: 2.017 on 6 and 1 DF,  p-value: 0.4922

BSL-SRT

lm(bsl_srt ~ nback_comb + 
     mr3d_sats +       
     corsi_corr +
     self_rating,
   #kirk_acc +
   #kbit_acc + 
   #dspan_corr,
   data = apt) %>% summary()
## 
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr + 
##     self_rating, data = apt)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.772 -3.277  0.507  2.495  5.864 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -46.179130  30.657088  -1.506   0.1663  
## nback_comb   75.472442  38.054691   1.983   0.0786 .
## mr3d_sats     1.883576   1.289609   1.461   0.1781  
## corsi_corr    0.434593   1.153233   0.377   0.7150  
## self_rating   0.007539   1.147214   0.007   0.9949  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.226 on 9 degrees of freedom
##   (106 observations deleted due to missingness)
## Multiple R-squared:  0.4738, Adjusted R-squared:  0.2399 
## F-statistic: 2.026 on 4 and 9 DF,  p-value: 0.1742
# wide version - pre-degree
lm(`bsl_srt_3rd year` ~ 
                                  `mr3d_sats_pre-degree` + 
                                  `corsi_corr_pre-degree` +
                                  `kirk_acc_pre-degree` +
                                   self_rating, 
                                   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + 
##     `kirk_acc_pre-degree` + self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      26      28 
##  0.4041 -2.8902 -2.4367  0.5007  1.5638  2.2072  3.0150 -2.9032  0.5392 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -6.0187    13.9944  -0.430    0.689
## `mr3d_sats_pre-degree`    1.3889     1.1608   1.196    0.298
## `corsi_corr_pre-degree`   1.2925     1.0540   1.226    0.287
## `kirk_acc_pre-degree`    12.9361    11.6096   1.114    0.328
## self_rating              -0.5175     1.3071  -0.396    0.712
## 
## Residual standard error: 3.156 on 4 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.6017, Adjusted R-squared:  0.2034 
## F-statistic: 1.511 on 4 and 4 DF,  p-value: 0.3496
# final assessments in 3rd year
lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` + 
                                  `mr3d_sats_3rd year` + 
                                  `mr2d_sats_3rd year` +
                                  `corsi_corr_3rd year` +
                                   self_rating,
                                  #`dspan_corr_3rd year`,
                                  data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
##  0.64448 -1.15297 -0.51955  1.11033  0.48620  0.18338  1.54004  0.07878 
##       28 
## -2.37069 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -52.77065   23.15888  -2.279    0.107  
## `nback_comb_3rd year`  60.41687   30.88973   1.956    0.145  
## `mr3d_sats_3rd year`    0.09283    1.08460   0.086    0.937  
## `mr2d_sats_3rd year`    3.64381    1.85882   1.960    0.145  
## `corsi_corr_3rd year`   2.40889    0.64130   3.756    0.033 *
## self_rating            -0.71772    0.94426  -0.760    0.502  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.959 on 3 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.8848, Adjusted R-squared:  0.6929 
## F-statistic:  4.61 on 5 and 3 DF,  p-value: 0.1192

Interpreting Grades

lm(grade_terp ~ nback_spat + 
     mr3d_sats + 
     corsi_corr +
     kirk_acc + 
     dspan_corr +
     self_rating,
   data = apt) %>% summary()
## 
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr + 
##     kirk_acc + dspan_corr + self_rating, data = apt)
## 
## Residuals:
##      66      69      70      73      75      77      78      80      81      86 
##  5.0088  0.6792 -4.9458  1.6493 -3.0451 -2.0404  0.5211 -1.7493  4.8945 -0.9723 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  341.0421    91.4250   3.730   0.0336 *
## nback_spat  -313.8872    99.3076  -3.161   0.0508 .
## mr3d_sats      6.3805     2.1097   3.024   0.0566 .
## corsi_corr    -7.3209     2.3355  -3.135   0.0519 .
## kirk_acc      59.7383    20.9992   2.845   0.0654 .
## dspan_corr   -52.5709    26.7487  -1.965   0.1441  
## self_rating    0.6754     2.2121   0.305   0.7801  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.609 on 3 degrees of freedom
##   (110 observations deleted due to missingness)
## Multiple R-squared:  0.8868, Adjusted R-squared:  0.6605 
## F-statistic: 3.918 on 6 and 3 DF,  p-value: 0.1449
# wide version - pre-degree
lm(`grade_terp_2nd year` ~ `nback_comb_pre-degree` +
     `mr3d_sats_pre-degree` + 
     `corsi_corr_pre-degree` +
     `kirk_acc_pre-degree` + 
     `dspan_corr_pre-degree`,
   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` + 
##     `dspan_corr_pre-degree`, data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.520  -1.366   1.526   2.695  10.381 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -3.6037    52.7366  -0.068    0.947
## `nback_comb_pre-degree`  40.0142    60.4054   0.662    0.529
## `mr3d_sats_pre-degree`    1.5724     1.5528   1.013    0.345
## `corsi_corr_pre-degree`   1.9683     3.1280   0.629    0.549
## `kirk_acc_pre-degree`    -0.5881    74.3808  -0.008    0.994
## `dspan_corr_pre-degree`  30.7403    63.5947   0.483    0.644
## 
## Residual standard error: 9.166 on 7 degrees of freedom
##   (17 observations deleted due to missingness)
## Multiple R-squared:  0.5111, Adjusted R-squared:  0.1619 
## F-statistic: 1.463 on 5 and 7 DF,  p-value: 0.3116
# final assessments in 3rd year
lm(`grade_terp_2nd year` ~ `nback_comb_3rd year` + 
     `mr3d_sats_3rd year` +
     `mr2d_sats_3rd year` +
     `corsi_corr_3rd year` +
     `dspan_corr_3rd year` +
     self_rating,
   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` + 
##     `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` + 
##     `dspan_corr_3rd year` + self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      26      28 
##  0.4441 -0.1519  0.2895  1.9748 -1.6086  0.7057 -1.0253 -0.6283 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -20.190     35.178  -0.574    0.668
## `nback_comb_3rd year`  -39.666     67.744  -0.586    0.663
## `mr3d_sats_3rd year`     3.433      1.662   2.066    0.287
## `mr2d_sats_3rd year`    14.624      4.168   3.509    0.177
## `corsi_corr_3rd year`    4.653      1.175   3.961    0.157
## `dspan_corr_3rd year`   68.163     49.598   1.374    0.400
## self_rating              2.481      1.552   1.599    0.356
## 
## Residual standard error: 2.956 on 1 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.9825, Adjusted R-squared:  0.8777 
## F-statistic: 9.371 on 6 and 1 DF,  p-value: 0.245

BSL>Eng interpreting

lm(terp_b2e ~ nback_spat +
     mr3d_sats + 
     corsi_corr +
     dspan_corr +
     self_rating,  
   data = apt) %>% summary()
## 
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr + self_rating, data = apt)
## 
## Residuals:
##    103    105    107    108    110    111    112    118 
## -3.714 -1.343  1.808  3.084 -3.811 -2.718  5.330  1.364 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   20.065     65.433   0.307    0.788
## nback_spat    60.044     89.747   0.669    0.572
## mr3d_sats      2.279      3.358   0.679    0.567
## corsi_corr     2.615      4.115   0.635    0.590
## dspan_corr   -27.812     72.266  -0.385    0.737
## self_rating   -1.871      3.927  -0.476    0.681
## 
## Residual standard error: 6.346 on 2 degrees of freedom
##   (112 observations deleted due to missingness)
## Multiple R-squared:  0.3849, Adjusted R-squared:  -1.153 
## F-statistic: 0.2503 on 5 and 2 DF,  p-value: 0.9081
# wide version - pre-degree
lm(`terp_b2e_3rd year` ~ `nback_comb_pre-degree` + 
     `mr3d_sats_pre-degree` + 
     `corsi_corr_pre-degree` +
     `dspan_corr_pre-degree`+
     self_rating,
     data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
## ALL 5 residuals are 0: no residual degrees of freedom!
## 
## Coefficients: (1 not defined because of singularities)
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               911.589         NA      NA       NA
## `nback_comb_pre-degree`  -464.608         NA      NA       NA
## `mr3d_sats_pre-degree`      1.394         NA      NA       NA
## `corsi_corr_pre-degree`    22.437         NA      NA       NA
## `dspan_corr_pre-degree` -1009.482         NA      NA       NA
## self_rating                    NA         NA      NA       NA
## 
## Residual standard error: NaN on 0 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:      1,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 4 and 0 DF,  p-value: NA
lm(`terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` +
     self_rating, 
   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
## -4.4451  0.3171  0.6291 -0.3183 -1.4933 -2.9145  2.7963  5.4287 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             66.4684     3.1264  21.260 4.27e-06 ***
## `mr3d_sats_pre-degree`   2.4042     1.1997   2.004    0.101    
## self_rating              0.2235     1.2903   0.173    0.869    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.698 on 5 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.4779, Adjusted R-squared:  0.2691 
## F-statistic: 2.289 on 2 and 5 DF,  p-value: 0.1969
# final assessments in 3rd year
lm(`terp_b2e_3rd year` ~ `nback_comb_3rd year` +
     `mr3d_sats_3rd year` + 
     `mr2d_sats_3rd year` +
     `corsi_corr_3rd year` +
     `dspan_corr_3rd year` +
     self_rating,
     data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##      13      15      17      18      20      21      22      28 
## -1.2305  1.8597  1.3958  0.1906  0.3307 -5.6281  2.6628  0.4190 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)            44.2229    80.3244   0.551    0.680
## `nback_comb_3rd year`  33.9926   128.3111   0.265    0.835
## `mr3d_sats_3rd year`   -3.1494     6.5776  -0.479    0.716
## `mr2d_sats_3rd year`    8.3609     8.1955   1.020    0.494
## `corsi_corr_3rd year`  -0.1087     4.4991  -0.024    0.985
## `dspan_corr_3rd year`  -8.7053    80.3181  -0.108    0.931
## self_rating             2.6857     4.9209   0.546    0.682
## 
## Residual standard error: 6.783 on 1 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.6487, Adjusted R-squared:  -1.459 
## F-statistic: 0.3077 on 6 and 1 DF,  p-value: 0.8785

Eng>BSL interpreting

lm(terp_e2b ~ nback_spat +
     mr3d_sats + 
     corsi_corr +
     dspan_corr +
     self_rating,
   data = apt) %>% summary()
## 
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr + 
##     dspan_corr + self_rating, data = apt)
## 
## Residuals:
##      103      105      107      108      110      111      112      116 
## -17.4064  -1.6379  -0.8478  -0.2062 -14.0278   0.3512  15.1243   7.4936 
##      118 
##  11.1570 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -74.3498   177.8746  -0.418    0.704
## nback_spat  124.0204   243.5084   0.509    0.646
## mr3d_sats     0.4579     6.6724   0.069    0.950
## corsi_corr    4.0471     6.6784   0.606    0.587
## dspan_corr   14.3122   168.1128   0.085    0.938
## self_rating  -1.0351     8.2803  -0.125    0.908
## 
## Residual standard error: 17.44 on 3 degrees of freedom
##   (111 observations deleted due to missingness)
## Multiple R-squared:  0.2709, Adjusted R-squared:  -0.9442 
## F-statistic: 0.223 on 5 and 3 DF,  p-value: 0.9301
# wide version - pre-degree
lm(`terp_e2b_3rd year` ~ `nback_comb_pre-degree` + 
     `mr3d_sats_pre-degree` + 
     `corsi_corr_pre-degree` +
     `dspan_corr_pre-degree` +
     self_rating,
   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` + 
##     `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
## ALL 6 residuals are 0: no residual degrees of freedom!
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)             -201.531         NA      NA       NA
## `nback_comb_pre-degree`  244.387         NA      NA       NA
## `mr3d_sats_pre-degree`     8.151         NA      NA       NA
## `corsi_corr_pre-degree`   17.899         NA      NA       NA
## `dspan_corr_pre-degree`  -17.186         NA      NA       NA
## self_rating              -18.894         NA      NA       NA
## 
## Residual standard error: NaN on 0 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:      1,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 5 and 0 DF,  p-value: NA
lm(`terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` +
     self_rating,
   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating, 
##     data = apt_wide)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.243  -5.679   1.404   2.243  18.096 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              62.273     10.114   6.157 0.000842 ***
## `mr3d_sats_pre-degree`    5.158      3.917   1.317 0.235955    
## self_rating               2.062      3.973   0.519 0.622317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.09 on 6 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.2997, Adjusted R-squared:  0.06625 
## F-statistic: 1.284 on 2 and 6 DF,  p-value: 0.3435
# final assessments in 3rd year
lm(`terp_e2b_3rd year` ~ `nback_comb_3rd year` +
     `mr3d_sats_3rd year` + 
     `mr2d_sats_3rd year` +
     `corsi_corr_3rd year` +
     `dspan_corr_3rd year` +
     self_rating,
   data = apt_wide) %>% summary()
## 
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` + 
##     `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` + 
##     self_rating, data = apt_wide)
## 
## Residuals:
##       13       15       17       18       20       21       22       26 
## -1.73737  3.30228  3.00420  2.93128 -1.47311 -9.67350  5.01160 -1.33559 
##       28 
## -0.02979 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            -59.757    103.971  -0.575   0.6235  
## `nback_comb_3rd year`  143.253    165.297   0.867   0.4775  
## `mr3d_sats_3rd year`    -8.983      4.862  -1.848   0.2059  
## `mr2d_sats_3rd year`    25.403      8.402   3.024   0.0942 .
## `corsi_corr_3rd year`    6.513      3.486   1.868   0.2027  
## `dspan_corr_3rd year`  -62.423     96.230  -0.649   0.5831  
## self_rating              4.204      4.274   0.984   0.4290  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 8.78 on 2 degrees of freedom
##   (21 observations deleted due to missingness)
## Multiple R-squared:  0.8768, Adjusted R-squared:  0.5073 
## F-statistic: 2.373 on 6 and 2 DF,  p-value: 0.3259

This concludes the BSL/SLI predictors analysis.